random_forest_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of random_forest_regression.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sandeep8064/Machine-learning-/blob/main/random_forest_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeamvpPVXuS_"
      },
      "source": [
        "# Random Forest Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2wvZ7SKXzVC"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVmESEFZX4Ig"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgbK_F8-X7em"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adBE4tjQX_Bh"
      },
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "x = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4S2fyIBYDcu"
      },
      "source": [
        "## Training the Random Forest Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "regressor = RandomForestRegressor(n_estimators=10,random_state=0)\n",
        "regressor.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ir4MH7Bisa0k",
        "outputId": "c4cdac61-bcce-4bf1-bb3d-9d9b7aa2b629"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(n_estimators=10, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IbsXbK3YM4M"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.predict([[6.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dIPE95vls1cE",
        "outputId": "0ff218d9-f5c4-4d58-e457-f162e114ea27"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([167000.])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLqF9yMbYTon"
      },
      "source": [
        "## Visualising the Random Forest Regression results (higher resolution)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_grid = np.arange(min(x), max(x), 0.01)\n",
        "X_grid = X_grid.reshape((len(X_grid), 1))\n",
        "plt.scatter(x, y, color = 'red')\n",
        "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
        "plt.title('Position VS Salary (Random Forest Regression)')\n",
        "plt.xlabel('Position')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "DwsvCb7GtG_H",
        "outputId": "ed6769e0-b050-4827-bc71-a37feeeaac94"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}